We are almost at the end of our Summer of Workflows series!
This week we are featuring the Label Studio Workflow inside ApertureDB Cloud:
See It In Action
Spin up Label Studio connected to your ApertureDB instance
Label & annotate images right where your data lives
Build labeled datasets faster without manual transfer of cloud bucket URLs.
Perfect for anyone building multimodal AI agents or applications and looking to streamline annotation + dataset creation.
Ready to try it yourself? Start here
Read The Docs | Explore The Code | Additional Resources
Only 2 workflows left in the summer series stay tuned!
Feedback always welcome we re building this for the AI/ML community!
ApertureDB
ApertureDB
If you want to see a live demo of how you can use workflows, do join us for our lunch & learn in the morning at 9am PT
https://lu.ma/vnabtolp
ApertureDB
@vishakha_gupta4 @hamza_afzal_butt The best part is that what the workflows do is open source. While the workflows on the cloud UI are a subset of possibilities, this repository has the all the detailed workings of workflows under the hood.
With this repository as a reference guide, following are the possibilities:
You may refer to what those scripts are doing to get a blue print for building your own workflow.
You may submit a PR. A PR for any custom workflow would be highly encouraged. TIA.
If it is a general enough workflow, it would eventually get published on the cloud UI too!
ApertureDB
@hamza_afzal_butt do join in the lunch & learn happening now - it's one of the things Luis can answer showing how to from the repo as Gautam described : https://lu.ma/vnabtolp
Hi Vishakha – How does ApertureDB compare to alternatives in terms of read/write speed and query performance on both small and large datasets? Additionally, does it have any unique optimizations or "special sauce" for faster token processing?
ApertureDB
@mceoin great question - we have some recent benchmarking results summarized here: https://docs.aperturedata.io/category/benchmarks--comparisons
Mainly, for vector search, we are anywhere between 2-10X faster in terms of KNN throughput and offer sub-10msec latencies on service side. For graph search, our prior evaluations against Neo4j put us sometimes over 30X faster. Mainly, ApertureDB continues to scale for very large workloads (Billion scale graphs so far and 10s and millions of embeddings per search space). We have optimizations when we load data - so far we have tested it more on parallel load of large number of blobs or images - we can extend that to faster token processing though we are yet to test it.
@vishakha_gupta4 30x Neo4j is very impressive. Will have to check it out!
ApertureDB
@mceoin let's set up time to chat - would love to understand your use case and see if we can collaborate.
LangDrive
This is a game-changer for AI developers! Congrats on the launch @ApertureDB
ApertureDB
@michael_vandi thanks a lot. We are happy to be working with you all!
This is the hidden missing piece in SO MANY ML workloads. Great work by the ApetureDB team!
ApertureDB
Thank you @aronchick we look forward to our collaborative examples coming in the near future to demonstrate how everyone can use these end to end even starting from edge to query
Love this! Super useful for devs. Congrats on the launch!
ApertureDB
@mahima_manik thank you for your support. Looking forward to integrating this with Datahawk!
TweetChat
ApertureDB
@peterbordes thank you ! we are seeing more and more people realize the need for the combined solution that we offer. It is hard to do vector in one, graph in another , data in a third place. Starts to wear people out as they scale and try to keep up with the rate at which AI is evolving